Optimal Gantry Crane PID Controller Based-on LQR With Prescribed Degree of Stability by Means of GA, PSO and SA Steven Bandong(a*,e), Rizky Cahya Kirana(b*,d), Yul Yunazwin Nazaruddin(c*,d), Endra Joelianto(c**,d,e)
a)Engineering Physics Graduate Program, Institut Teknologi Bandung, Indonesia *bandong.steven[at]gmail.com
b)Instrumentation and Control Graduate Program, Institut Teknologi Bandung, Indonesia
*rizkycahyakirana[at]gmail.com
c)Instrumentation and Control Research Group, Institut Teknologi Bandung, Indonesia
*yul[at]tf.itb.ac.id, **ejoel[at]tf.itb.ac.id
d)National Center for Sustainable Transportation Technology (NCSTT), Institut Teknologi Bandung, Indonesia
e)University Center of Excellence Artificial Intelligence on Vision, NLP & Big Data Analytics (U-CoE AI-VLB), Institut Teknologi Bandung, Indonesia
Abstract
Trade between islands and countries is increasing in the current era of globalization which also increases the traffic of goods at ports. Rubber Tyred Gantry Crane (RTGC) is an important component in the distribution chain at the seaports which act as a loading and unloading machine at the container yard. However, heavy trade traffic is likely to cause fatigue and negligence if the RTGC is operated manually. Therefore, it is necessary to automate RTGC by applying optimal control. The paper introduces an alternative approach to design optimal PID controller which is built from LQR method combined with prescribed degree of stability method for achieving the required transient and steady state responses of RTGC in the port. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are applied to select the suitable stability degree value and weighting matrices in the LQR cost function. Simulation results indicate that GA is able to provide the optimal PID controller in following the reference trajectory and minimizing the swing angle better than PSO and SA.
Keywords: RTGC Automation, PID, LQR, Prescribed Degree of Stability, Optimization
